I spent a week earlier this month at the Joint Statistical Meetings (JSM)—an annual convocation of “data scientists”, as some of these number crunchers now deem themselves.  But most statisticians remain ‘old school’ as evidenced by this quote:

“Some time during the past couple of years, statistics became data sciences older, more boring sibling that always played by the rules.”

— Nathan Yau*

I tend to agree—being suspicious of changes in titles as a cover for shenanigans.  It seems to me that “data science” provides a smoke screen to take unwarranted leaps from shaky numbers.  As the shirt sold at JSM by American Statistical Association (ASA) says, “friends don’t let friends extrapolate.”

*Incorrectly attributed initially (my mistake) to Carnegie Mellon statistics professor Cosma Shalizi, who was credited by Yau for speaking up on this subject.

With this intriguing title Richard Feinberg and Howard Wainer draw readers of Volume 20, Number 4 into what might have been a dry discourse: How contributors to The Journal of Computational and Graphical Statistics rely mainly on tables to display data.  Given that “Graphical” is in the title of this publication, it begs the question on whether this method of for presenting statistics really works.

When working on the committee that developed the ASTM 1169-07 Standard Practice for Conducting Ruggedness Tests, I introduced the half-normal plot for selecting effects from two-level factorial experiments.  Most of the committee favored this, but one individual – a professor emeritus from a top school of statistics – resisted the introduction of this graphical tool.  He believed that only numerical methods, specifically analysis of variance (ANOVA) tables, could support objective decisions for model selection.  My comeback was to dodge the issue by simply using graphs and tables – this need not be an either/or choice.  Why not do both, or merge them by putting number on to graphs – the best of both worlds?

“A heavy bank of figures is grievously wearisome to the eye, and the popular mind is as incapable of drawing any useful lessons from it as of extracting sunbeams from cucumbers.”

— Economists (brothers) Farquhar and Farquhar (1891)

In their article which can be seen here Feinberg and Wainer take a different tack (path of least resistance?): Make tables look more like graphs.  Here are some of their suggestions for doing so:

• Round data to 3 digits or less.
• Line up comparable numbers by column, not row.
• Provide summary statistics, in particular medians.
• Don’t default to alphabetical or some other arbitrary order: Stratify by size or some other meaningful attribute.
• Call out data that demands attention by making it bold and/or bigger and/or boxing it.
• Insert extra space between rows or columns of data where they change greatly (gap).

Check out the remodeled table on arms transfers which makes it clear that, unlike the uptight USA, the laissez faire French will sell to anyone.  It would be hard to dig that nugget out of the original data compilation.

Yesterday I attended a fun webinar on Interactive Statistics Education by Dale Berger of Claremont Graduate University.  Because I was multitasking  (aka “continuous partial attention” — ha ha) at work while attending this webinar my report provides just the highlights.  However, you can figure out for yourself what they (the stats dept at Claremont) have to offer by going to this web page offering WISE (Web Interface for Statistics Education) tutorials and applets.*

After the presentation a number of educators brainstormed on interactive stats.  David Lane of Rice U (author of many stat applets) suggested the use of “interactive clickers” – see this short (< 2 min.) newscast, for example.  I wonder what happen when a majority vote for the wrong answer?  For some teachers it might be easiest just to declare the most popular response as the correct answer.  That would be consistent with the way things seem to be going in politics nowadays. ; )

*Just for fun try the Investigating the Central Limit Theorem (CLT) applet (click the link from the page referenced above or simply click here).  This would be a good applet to provide when illustrating CLT using dice (such as is done in this in-class exercise developed by two professors from De Anza College). In this case, pick the uniform Population and sample size 2.  Then Draw a Sample repeatedly, and, finally, just Draw 100 samples.  Repeat this exercise with sample size 5 a la the game of Yahtzee (a favorite in my youth). Notice how as n goes up the distribution of averages becomes more normal and narrower. That’s the power of averaging.

(Warning: Quirky material ahead =>)

Seeing this CBS News about Maine legalizing switchblades for one-armed people reminded me of a riddle about limbs that’s posed by some statisticians for educational purposes.  Here it is: “The great majority of people in [fill in your country here] have more than the average number of [choose either arms or legs here].”

For an answer {UK, legs}, see this posting on averages by Kevin McConway, Professor of Applied Statistics in the Department of Mathematics and Statistics at The Open University.  I heard this riddle also from Hans Rosling in his BBC TV program on “The Joy of Statistics.”*  He spoke of his home country of Sweden, whose inhabitants on average have 1.999 legs.

I’m quitting while I’m ahead.  Oops, this makes me wonder if I have an average number of heads – a scary thought, my hunch being that I’m below average for this.  I never imagined that averages could be so creepy!

*See this StatsMadeEasy blog on Rosling